Field of Invention
Embodiments of the invention relate generally to automata processors, and more specifically, to implementing RANDOM FORESTS®, or random decision forest models, utilizing automata processors.
Description of Related Art
Certain apparatus, including computational electronic devices and systems, may include a number of processing resources (e.g., one or more processors), which may retrieve and execute instructions and store the results of the executed instructions to a suitable location. For example, the processing resources may include a number of functional units, arithmetic units, and similar circuitry to execute instructions by performing a number of Boolean logical operations and arithmetic functions. One particular processing resource may include an automata-based processing resource, which may be suitable for use in applications such as, for example, network security, computational biology, image processing, text searching, and so forth. These automata-based processing resources, may include, or may be described for example, by a number of state elements and signal transitions among these state elements. Each state element of the automata-based processing resources may store and/or recognize one or more data values.
Similarly, a variety of applications employ ensemble learning models utilizing, for example, a collection of decision trees (e.g., RANDOM FORESTS®, or random decision forest models) to quickly and accurately classify an input based on a vector of features. The execution of these models on existing Von Neumann processor architectures may be memory-bound, and further architecture-conscious optimizations to accelerate by coercing these computations into complex memory hierarchies have only achieved limited success. It may be useful to provide devices and methods to increase processing and performance efficiency of computing machine learning models such as RANDOM FORESTS®, or random decision forest model.